def __init__(self, nii, spacing=_default_spacing, plot_type="grid", **kwargs):
"""Load deformation field.
Parameters
----------
nii : str/array/nifti
Source of the image data to load. This can be either:
(a) The path to a NIfTI file;
(b) A nibabel.nifti1.Nifti1Image object;
(c) The path to a file containing a NumPy array;
(d) A NumPy array.
"""
Image.__init__(self, nii, **kwargs)
if not self.valid:
return
if self.data.ndim != 5:
raise RuntimeError(
f"Deformation field in {nii} must contain a " "five-dimensional array!"
)
self.data = self.data[:, :, :, 0, :]
self.set_spacing(spacing)
def __init__(
self,
nii=None,
dose=None,
mask=None,
jacobian=None,
df=None,
structs=None,
multi_structs=None,
timeseries=None,
struct_colors=None,
structs_as_mask=False,
struct_names=None,
compare_structs=False,
comp_type="auto",
ignore_empty_structs=False,
ignore_unpaired_structs=False,
structs_to_keep=None,
structs_to_ignore=None,
autoload_structs=True,
mask_threshold=0.5,
**kwargs,
):
"""Load a MultiImage object.
Parameters
----------
nii : str/nifti/array
Path to a .nii/.npy file, or an nibabel nifti object/numpy array.
title : str, default=None
Title for this image when plotted. If None and <nii> is loaded from
a file, the filename will be used.
dose : str/nifti/array, default=None
Path or object from which to load dose field.
mask : str/nifti/array, default=None
Path or object from which to load mask array.
jacobian : str/nifti/array, default=None
Path or object from which to load jacobian determinant field.
df : str/nifti/array, default=None
Path or object from which to load deformation field.
structs : str/list, default=None
A string containing a path, directory, or wildcard pointing to
nifti file(s) containing structure(s). Can also be a list of
paths/directories/wildcards.
struct_colors : dict, default=None
Custom colors to use for structures. Dictionary keys can be a
structure name or a wildcard matching structure name(s). Values
should be any valid matplotlib color.
structs_as_mask : bool, default=False
If True, structures will be used as masks.
struct_names : list/dict, default=None
For multi_structs, this parameter will be used to name
the structures. Can either be a list (i.e. the first structure in
the file will be given the first name in the list and so on), or a
dict of numbers and names (e.g. {1: "first structure"} etc).
compare_structs : bool, default=False
If True, structures will be paired together into comparisons.
mask_threshold : float, default=0.5
Threshold on mask array; voxels with values below this threshold
will be masked (or values above, if <invert_mask> is True).
"""
# Flags for image type
self.dose_as_im = False
self.dose_comp = False
self.timeseries = False
# Load the scan image
if nii is not None:
Image.__init__(self, nii, **kwargs)
self.timeseries = False
# Load a dose field only
elif dose is not None and timeseries is None:
self.dose_as_im = True
Image.__init__(self, dose, **kwargs)
dose = None
# Load a timeseries of images
elif timeseries is not None:
self.timeseries = True
dates = self.get_date_dict(timeseries)
self.dates = list(dates.keys())
if "title" in kwargs:
kwargs.pop("title")
self.ims = {
date: Image(file, title=date, **kwargs) for date, file in dates.items()
}
Image.__init__(self, dates[self.dates[0]], title=self.dates[0], **kwargs)
self.date = self.dates[0]
else:
raise TypeError("Must provide either <nii>, <dose>, or " "<timeseries!>")
if not self.valid:
return
# Load extra overlays
self.load_to(dose, "dose", kwargs)
self.load_to(mask, "mask", kwargs)
self.load_to(jacobian, "jacobian", kwargs)
self.load_df(df)
# Load structs
self.comp_type = comp_type
self.load_structs(
structs,
multi_structs,
names=struct_names,
colors=struct_colors,
compare_structs=compare_structs,
ignore_empty=ignore_empty_structs,
ignore_unpaired=ignore_unpaired_structs,
comp_type=comp_type,
to_keep=structs_to_keep,
to_ignore=structs_to_ignore,
autoload=autoload_structs
)
# Mask settings
self.structs_as_mask = structs_as_mask
if self.has_structs and structs_as_mask:
self.has_mask = True
self.mask_threshold = mask_threshold
self.set_masks()
def __init__(
self,
shape,
filename=None,
origin=(0, 0, 0),
voxel_sizes=(1, 1, 1),
intensity=-1024,
noise_std=None,
):
"""Create data to write to a NIfTI file, initially containing a
blank image array.
Parameters
----------
shape : int/tuple
Dimensions of the image array to create. If an int is given, the
image will be created with dimensions (shape, shape, shape).
filename : str, default=None
Name of output NIfTI file. If given, the NIfTI file will
automatically be written; otherwise, no file will be written until
the "write" method is called.
origin : tuple, default=(0, 0, 0)
Origin in mm for the image.
voxel_sizes : tuple, default=(1, 1, 1)
Voxel sizes in mm for the image.
intensity : float, default=-1000
Intensity in HU for the background of the image.
noise_std : float, default=None
Standard deviation of Gaussian noise to apply to the image.
If None, no noise will be applied.
"""
# Create image properties
self.shape = make_three(shape)
voxel_sizes = [abs(v) for v in make_three(voxel_sizes)]
self.affine = np.array(
[
[
-voxel_sizes[0],
0,
0,
origin[0] + (self.shape[0] - 1) * voxel_sizes[0],
],
[0, voxel_sizes[1], 0, origin[1]],
[0, 0, voxel_sizes[2], origin[2]],
[0, 0, 0, 1],
]
)
self.max_hu = 0 if noise_std is None else noise_std * 3
self.min_hu = -self.max_hu if self.max_hu != 0 else -20
self.noise_std = noise_std
self.bg_intensity = intensity
self.background = self.make_background()
self.shapes = []
self.structs = []
self.groups = {}
self.shape_count = {}
self.translation = None
self.rotation = None
# Initialise as Image
Image.__init__(self, self.background, affine=self.affine)
# Write to file if a filename is given
if filename is not None:
self.filename = os.path.expanduser(filename)
self.write()